1 research outputs found
Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders
We introduce a vortex phase transform with a lenslet-array to accompany
shallow, dense, ``small-brain'' neural networks for high-speed and low-light
imaging. Our single-shot ptychographic approach exploits the coherent
diffraction, compact representation, and edge enhancement of Fourier-tranformed
spiral-phase gradients. With vortex spatial encoding, a small brain is trained
to deconvolve images at rates 5-20 times faster than those achieved with random
encoding schemes, where greater advantages are gained in the presence of noise.
Once trained, the small brain reconstructs an object from intensity-only data,
solving an inverse mapping without performing iterations on each image and
without deep-learning schemes. With this hybrid, optical-digital, vortex
Fourier encoded, small-brain scheme, we reconstruct MNIST Fashion objects
illuminated with low-light flux (5 nJ/cm) at a rate of several thousand
frames per second on a 15 W central processing unit, two orders of magnitude
faster than convolutional neural networks